TOP 10 Artificial Intelligence (AI) Startups You should Know

Right now, Artificial Intelligence is the massive technological boom that redefines the way we think, the way we live and do business. Having no geographical boundaries, ai tech almost guarantees success for all aspiring startups. Is it so, and what makes top AI companies so successful? Let’s analyze the current state of the market today and find an answer.

Artificial Intelligence: market review

If you want to stay on top of things concerning AI technology today just take a look at the most recent state reports from the AI Now Institute at New York University and the annual AI Index from the Palo Alto initiative group. Both demonstrate the cosmic rate of AI progress accelerating quickly in two ways: intensively – within its sphere, and extensively – by encompassing other fields.

Under the overwhelming wave of AI-excitement, almost every startup seeks to invest lots of money to add these two magical letters into their titles. Do they have an aim to succeed in performing complex tasks or just making hype? One way or another, according to CBInsights, in the second quarter of 2019, companies made investments to AI up to $7.4 billion. That’s not all, IDC estimated that this number will grow to more than $98 billion by 2023.

All these cosmic figures mean only one thing: this cutting-edge technology is here, it is ready to change our future, and we need to be prepared for this. Whether you want to be in the same line with hot machine learning startups, or just to keep up with the market today, you need to monitor it. In the follow-up section, we will shed light on the most striking AI companies concerning software development, data management, and cybersecurity. Grab your coffee and take the first step to what you are interested in!

Most promising Tech AI startups

From Silicon Valley to Japan and Finnish AI startups, everyone is battling to develop robust cutting-edge technology. But just like with any other gold rush, only a few will go home winners. Let’s catch up with the most promising players and find out what makes them so. These companies are categorized by focus areas covering software development, data management, and cybersecurity.

While a plethora of startups works to automate routine tasks for drivers, lawyers, and financiers, there is less talk about IT specialists. But Mabl tries to change this situation. Focusing on the automation of web-apps testing, this startup has a strong ambition either to help QA engineers maintain tests and find bugs or replace them at all. Although the steps are still unclear, the startup is already attracting huge investments and showing striking results.

So, how is Mabl working? In brief, it outputs a DevTestOps platform that runs functional tests, and then the most interesting part begins – the more it learns, the more it does. Current functionality looks standard: the user opens a site and marks key results, the plugin, in turn, remembers the sequence of actions. The system then regularly repeats these actions and checks whether the answers have gone through any changes. Alert pops up in case of any inconsistency – not so difficult as it may seem at first glance, right?

QA experts know how much time is spent on full manual regression of the UI, including various operating systems, web browsers, screen resolutions, and more. Applitools’ developers, in turn, have decided to save you the trouble. Based on the user interface, Applitools system aims at the visual monitoring of applications and websites in real-time. It allows testing the look & feel of user experience and ensuring that every element appears correctly.

Another remarkable ML-driven startup leveraging AI for testing. Just like the previous two, Test.ai strives to help QA engineers and software developers stop writing tedious test automation scripts. What is the main secret of Test.ai success? This platform tests thousands of applications in parallel without the need to code. This leads to shorter testing cycles and faster time to market.

All you need to know about DeepCode is that this startup strives to make the life of QA engineers and software developers easier. It is an automated code review system based on semantic code analysis. It is mixed with neural network training using Big Data. The most pleasant thing about this startup is that GitHub public repository code is used as a database for training the network.

The developers claim that DeepCode can answer not only the question “how many errors are there in the code”, but also provide information on the number of new features and potential problems with the existing codebase. It automatically learns from millions of available software programs and uses this knowledge to make key suggestions. By the way, now the project is already available in test mode.

Holding the most creative title among top AI startups, source{d} is a project made for developers by developers. It focuses on providing open-source tools that allow to analyze large-scale code analysis and machine learning on source code. Applying neural networks, it goes through analyzing over 17 million software repositories from more than 6,6 million developers. Ultimately, source{d} copes with understanding code, and it does it well.

If you’re generating a significant amount of data, Dataiku helps to find a meaning behind it. This platform supports dozens of database formats and sources like Hadoop or NoSQL. All-in-all, Dataiku is great for visualizing data, cleaning the data sets, running some algorithms, building a machine learning model, deploying it and more.

The amount of data we produce every day is enormous. Cleaning data manually is no longer an easy solution, and it is the right time when AI comes to the rescue. The main product of Trifacta is AI-powered tool for implementing effective data wrangling and visualization. This solution is good for all possible kinds of data cleaning tasks and improving data quality. It makes data processing automated, speedy, and intuitive. It is not surprising that more than 20 thousand companies use Trifacta as their business intelligence model.

Do you want to improve your data management tasks? Just add water, or adjust the H2O.ai cutting-edge solution. Using in-memory machine learning algorithms and deep learning support, H2O.ai provides several tools for saving companies from boring data collection tasks. It supports several deployment options, including on a single node, on a cluster of several nodes, as well as on Hadoop or Apache Spark clusters. This platform is especially great for data scientists and developers. By the way, it is written in Java and natively supports the Java API.

What is Anodot working on? The title of this startup speaks for itself – Anomaly Detection is the key task for the Anodot team who strives to show the best results among other similar projects, and they succeed on this task well. Among their customers are such well-known names like AOL, LivePerson, Microsoft, Pandora, and Wix. Among their application sectors is business health, user behavior, IT ops, machine learning processes, and even IoT.

Last but not least AI startup in our list, Vectra AI is mainly famous due to developing Cognito platform, a real-time threat-detection system. This system is built on conducting processes like tracking the network traffic, extracting relevant metadata, and ingesting external threat intelligence. All-in-all, it automates tasks that are normally done by security analysts and so reduces human intervention to a great extent.

But, are there any drawbacks? Cognito is bad for taking any actions automatically to encounter threats, thus it is categorized as a threat-detection system rather than a threat-prevention system. However, this does not prevent a startup from holding on to the first positions and making huge investments in its further development.

Conclusion on Artificial Intelligence development

We have covered the top 10 most promising AI companies to keep an eye on. What is the key tendency among them? Without any exaggeration, it is redefining the software and testing environment and automating routine tasks.

But, if talking about QA services, does it mean testers will soon be replaced with AI? Well, we at QATestLab believe AI-powered approach is rather an ally than a foe. Intuition can’t be duplicated via algorithms, real testers are still an important part of the puzzle.

‘Artificial Intelligence is like an artificial plant. It gives many of the same benefits but it’s not the real thing.’ – this phrase dropped out by James Tagg speaks a lot.

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